These files don't follow the usual pattern: In general the files torch/csrc/X torch/csrc/cuda/X
both include the generic file torch/csrc/generic/X, where torch/csrc/X includes the cpu implementations and torch/csrc/cuda/X includes the cuda implementations.
(Aside: this is probably not the best structure, the torch/csrc/X fiels should probably be moved to torch/csrc/cpu/X).
utils.cpp combines these so that torch/csrc/utils.cpp has cuda specific code. This makes it impossible to declare a single THTensor and THCTensor template type (i.e. THPPointer<_THTensor>, THPointer<_THCTensor>).
Changelist:
- Move *.c to *.cpp
- Change includes of ".c" to ".cpp"
- A bunch of cmake configuration modifying CMAKE_C_FLAGS changed
to CMAKE_CXX_FLAGS or add_compile_options, because if you do CMAKE_C_FLAGS it only applies when you compile C code
- Explicitly cast void* to T* in a number of places
- Delete extern "C" { ... } blocks; instead, properly apply TH_API to everything that should have it (TH_API handles extern "C")
- Stop using stdatomic.h, instead, use <atomic>. This resulted in a bunch of placement-new/delete to be "totally properly correct"
- Refactor of THLongStorageView to not have static constructor methods (since it no longer has a copy/move constructor)
- Documentation about how the TH C interface (and extern C business) works
- Note that THD master_worker mode is dead
- C++ headers in TH libraries are given .hpp suffix, to make it less likely that you'll confuse them with the C-compatible headers (now suffixed .h)
- New function THCStream_stream and THCStream_device to project out fields of THCStream instead of accessing fields directly
- New function THStorage_(retainIfLive), which is equivalent to a retain but only if the refcount is greater than zero.
- In general, I tried to avoid using hpp headers outside of ATen/TH. However, there were a few places where I gave up and depended on the headers for my own sanity. See Note [TH abstraction violation] for all the sites where this occurred. All other sites were refactored to use functions
- Some extra Werror fixes (char* versus const char*)
* Use ATen infer_size implementation rather than TH.
The only substantitive difference between the two implementations is in how empty sizes are handled;
in ATen these are treated as scalars (i.e., can be expanded to anything), whereas in TH they are treated
as a special case of empty tensors (i.e., can't be expanded to anything). Therefore, this change is
necessary to support scalars (0-dimensional tensors). We could also take a bool parameter for determining
how we treat empty tensors but this seems unnecessary: if one tries to expand an empty tensors (as a result
of an infer_size calculation), the expansion will fail.
* Make changes for review.
* Attempt to fix windows build.
* long -> int.
The Tensor and Variable classes are being merged in Python. This means
that all interfaces to C++ must accept Variables where they previously
accepted Tensors.
Setting torch.utils.backcompat.broadcast.warning.enabled=True
will cause Python warnings in the case where broadcast occurs
but previously 1-d view style pointwise ops occured.
The core autograd Variable, Function, and Engine no longer depend on the
Python API. This let's us implement functions in C++. In the future, we
can also multithread engine and release the GIL for most of the
non-Python backwards.
arguments.
For example:
>>> torch.randn(5, 5).geqrf('invalid arg')
TypeError: geqrf received an invalid combination of arguments - got (str), but expected ()
See issue #20
The torch.Size class is a tuple subclass which distinguishes sizes from
other tuples so that torch.Tensor(size) is interpreted as size instead
of data.
